Deciphering the routes of invasion of Drosophila suzukii by means of ABC random forest

Fraimout Antoine, Debat Vincent, Fellous Simon, Hufbauer Ruth A., Foucaud Julien, Pudlo Pierre, Marin Jean-Michel, Price Donald K., Cattel Julien, Chen Xiao, Deprá Marindia, Duyck Pierre François, Guedot Christelle, Kenis Marc, Kimura Masahito T., Loeb Gregory, Loiseau Anne, Martinez-Sañudo Isabel, Pascual Marta, Richmond Maxi Polihronaki, Shearer Peter, Singh Nadia, Tamura Koichiro, Xuereb Anne, Zhang Jinping, Estoup Arnaud. 2017. Deciphering the routes of invasion of Drosophila suzukii by means of ABC random forest. Molecular Biology and Evolution, 34 (4) : pp. 980-996.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Quartile : Outlier, Sujet : GENETICS & HEREDITY / Quartile : Outlier, Sujet : EVOLUTIONARY BIOLOGY / Quartile : Outlier, Sujet : BIOCHEMISTRY & MOLECULAR BIOLOGY

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Anthropologie-Ethnologie; Psychologie-éthologie-ergonomie

Abstract : Deciphering invasion routes from molecular data is crucial to understanding biological invasions, including identifying bottlenecks in population size and admixture among distinct populations. Here, we unravel the invasion routes of the invasive pest Drosophila suzukii using a multi-locus microsatellite dataset (25 loci on 23 worldwide sampling locations). To do this, we use approximate Bayesian computation (ABC), which has improved the reconstruction of invasion routes, but can be computationally expensive. We use our study to illustrate the use of a new, more efficient, ABC method, ABC random forest (ABC-RF) and compare it to a standard ABC method (ABC-LDA). We find that Japan emerges as the most probable source of the earliest recorded invasion into Hawaii. Southeast China and Hawaii together are the most probable sources of populations in western North America, which then in turn served as sources for those in eastern North America. European populations are genetically more homogeneous than North American populations, and their most probable source is northeast China, with evidence of limited gene flow from the eastern US as well. All introduced populations passed through bottlenecks, and analyses reveal five distinct admixture events. These findings can inform hypotheses concerning how this species evolved between different and independent source and invasive populations. Methodological comparisons indicate that ABC-RF and ABC-LDA show concordant results if ABC-LDA is based on a large number of simulated datasets but that ABC-RF out-performs ABC-LDA when using a comparable and more manageable number of simulated datasets, especially when analyzing complex introduction scenarios. (Résumé d'auteur)

Mots-clés Agrovoc : Drosophila, Ravageur des plantes, Distribution géographique, Méthode statistique, Dynamique des populations, Écologie animale, Génétique des populations, Modèle de simulation

Mots-clés géographiques Agrovoc : Hawaï, Chine, Amérique du Nord

Mots-clés complémentaires : Drosophila suzukii

Classification Agris : L20 - Animal ecology
U10 - Computer science, mathematics and statistics
H10 - Pests of plants

Champ stratégique Cirad : Axe 4 (2014-2018) - Santé des animaux et des plantes

Agence(s) de financement européenne(s) : European Commission

Programme de financement européen : FP7

Projet(s) de financement européen(s) : Strategies to develop effective, innovative and practical approaches to protect major European fruit crops from pests and pathogens

Auteurs et affiliations

  • Fraimout Antoine, MNHN (FRA)
  • Debat Vincent, MNHN (FRA)
  • Fellous Simon, INRA (FRA)
  • Hufbauer Ruth A., Colorado State University (USA)
  • Foucaud Julien, INRA (FRA)
  • Pudlo Pierre, Université Aix-Marseille (FRA)
  • Marin Jean-Michel, Université de Montpellier (FRA)
  • Price Donald K., University of Hawaii (USA)
  • Cattel Julien, Université Claude Bernard (FRA)
  • Chen Xiao, Yunnan Agricultural University (CHN)
  • Deprá Marindia, UFRGS (BRA)
  • Duyck Pierre François, CIRAD-BIOS-UMR PVBMT (REU) ORCID: 0000-0001-5484-1970
  • Guedot Christelle, University of Wisconsin (USA)
  • Kenis Marc, CABI (GBR)
  • Kimura Masahito T., Hokkaido University (JPN)
  • Loeb Gregory, Cornell University (USA)
  • Loiseau Anne, INRA (FRA)
  • Martinez-Sañudo Isabel, Università degli Studi-Padova (ITA)
  • Pascual Marta, Universitat de Barcelona (ESP)
  • Richmond Maxi Polihronaki, California State University (USA)
  • Shearer Peter, North Carolina State University (USA)
  • Singh Nadia, MNHN (FRA)
  • Tamura Koichiro, University of Tokyo (JPN)
  • Xuereb Anne, INRA (FRA)
  • Zhang Jinping, CAAS (CHN)
  • Estoup Arnaud, INRA (FRA)

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